This dataset gathers results of monthly sampling with a WP2 plankton net within the Gironde plume (Bay of Biscay) in 2008 from March to August, as part of the ECLAIR suite of surveys. The sampling in May was part of the ECLAIR time-series but was performed onboard the THALASSA vessel during the PELGAS 2008 survey. Results are made of anchovy and sardine egg abundances, as well as size-fractionnated zooplankton biomass.

This visualization product displays the marine litter material categories percentage per trawl. EMODnet Chemistry included the collection of marine litter in its 3rd phase. Since the beginning of 2018, data of seafloor litter collected by international fish-trawl surveys have been gathered and processed in the EMODnet Chemistry Marine Litter Database (MLDB). The harmonization of all the data has been the most challenging task considering the heterogeneity of the data sources, sampling protocols (OSPAR and MEDITS protocols) and reference lists used on a European scale. Moreover, within the same protocol, different gear types are deployed during fishing bottom trawl surveys. Unlike other EMODnet seafloor litter products, all trawls surveyed since 2007 are included in this map even if the wingspread and/or the distance are unknown. Only surveys with an unknown number of items were excluded from this product. Harmonization of the material categories between ICES and MEDITS lists has been performed and the following calculation has been applied: Material % = (∑Number of items of each material category*100)/(∑Number of items of all material categories) More information on data processing and calculation are detailed in the document attached. Warning: the absence of data on the map doesn't necessarily mean that they don't exist, but that no information has been entered in the Marine Litter Database for this area.

Develop parentage assignment panels using genetic fingerprinting of pearl oysters for use in commercial hatcheries and research to manage pedigrees in order to limit the risks of loss of genetic variability and increased inbreeding of commercial lines.

This map presents all layers corresponding to "Renting and leasing of water transport equipment" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18518354&IntKey=18518474&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : Business indicators per country Number of persons employed and number of employees in full time equivalent units per NUTS 3 unit of the Atlantic Area

This map presents all layers corresponding to "Marine fishing" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18495674&IntKey=18495704&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : Business indicators per country Number of persons employed on Atlantic pits and rigs Overall gas volume produced Overall volume produced from Atlantic gas terminals

Water body dissolved oxygen concentration - Monthly Climatology for the European Seas for the period 1960-2020 on the domain from longitude -45.0 to 70.0 degrees East and latitude 24.0 to 83.0 degrees North. Data Sources: observational data from SeaDataNet/EMODnet Chemistry Data Network. Description of DIVA analysis: The computation was done with the DIVAnd (Data-Interpolating Variational Analysis in n dimensions), version 2.7.2, using GEBCO 30sec topography for the spatial connectivity of water masses. Horizontal correlation length and vertical correlation length vary spatially depending on the topography and domain. Depth range: 0.0, 5.0, 10.0, 15.0, 20.0, 25.0, 30.0, 35.0, 40.0, 45.0, 50.0, 55.0, 60.0, 65.0, 70.0, 75.0, 80.0, 85.0, 90.0, 95.0, 100.0, 125.0, 150.0, 175.0, 200.0, 225.0, 250.0, 275.0, 300.0, 325.0, 350.0, 375.0, 400.0, 425.0, 450.0, 475.0, 500.0, 550.0, 600.0, 650.0, 700.0, 750.0, 800.0, 850.0, 900.0, 950.0, 1000.0, 1050.0, 1100.0, 1150.0, 1200.0, 1250.0, 1300.0, 1350.0, 1400.0, 1450.0, 1500.0, 1550.0, 1600.0, 1650.0, 1700.0, 1750.0, 1800.0, 1850.0, 1900.0, 1950.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0, 2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0, 3600.0, 3700.0, 3800.0, 3900.0, 4000.0, 4100.0, 4200.0, 4300.0, 4400.0, 4500.0, 4600.0, 4700.0, 4800.0, 4900.0, 5000.0, 5100.0, 5200.0, 5300.0, 5400.0, 5500.0 m. Units: umol/l. The horizontal resolution of the produced DIVAnd analysis is 0.25 degrees.

160 whole genomes sequences obtained from 160 individual fish samples representing about 100 different species present in Gulf of Lion, and bay of Biscay.

''' Short description: ''' For the Mediterranean Sea - the CNR diurnal sub-skin Sea Surface Temperature (SST) product provides daily gap-free (L4) maps of hourly mean sub-skin SST at 1/16° (0.0625°) horizontal resolution over the CMEMS Mediterranean Sea (MED) domain, by combining infrared satellite and model data (Marullo et al., 2014). The implementation of this product takes advantage of the consolidated operational SST processing chains that provide daily mean SST fields over the same basin (Buongiorno Nardelli et al., 2013). The sub-skin temperature is the temperature at the base of the thermal skin layer and it is equivalent to the foundation SST at night, but during daytime it can be significantly different under favorable (clear sky and low wind) diurnal warming conditions. The sub-skin SST L4 product is created by combining geostationary satellite observations aquired from SEVIRI and model data (used as first-guess) aquired from the CMEMS MED Monitoring Forecasting Center (MFC). This approach takes advantage of geostationary satellite observations as the input signal source to produce hourly gap-free SST fields using model analyses as first-guess. The resulting SST anomaly field (satellite-model) is free, or nearly free, of any diurnal cycle, thus allowing to interpolate SST anomalies using satellite data acquired at different times of the day (Marullo et al., 2014). [https://help.marine.copernicus.eu/en/articles/4444611-how-to-cite-or-reference-copernicus-marine-products-and-services How to cite] '''DOI (product) :''' https://doi.org/10.48670/moi-00170

Understanding the spatial and temporal preferences of toxic phytoplankton species is of paramount importance in managing and predicting harmful events in aquatic ecosystems. In this study we address the realised niche of the species Alexandrium minutum, Pseudo-nitzschia fraudulenta and P. australis. We used them to highlight distribution patterns at different scales and determine possible drivers. To achieve this, we have developed original procedures coupling niche theory and habitat suitability modelling using abundance data in four consecutive steps: 1) Estimate the realised niche applying kernel functions. 2) Assess differences between the species’ niche as a whole and at the local level. 3) Develop habitat and temporal suitability models using niche overlap procedures. 4) Explore species temporal and spatial distributions to highlight possible drivers. Data used are species abundance and environmental variables collected over 27 years (1988-2014) and include 139 coastal water sampling sites along the French Atlantic coast. Results show that A. minutum and P. australis niches are very different, although both species have preference for warmer months. They both respond to decadal summer NAO but in the opposite way. P. fraudulenta realised niche lies in between the two other species niches. It also prefers warmer months but does not respond to decadal summer NAO. The Brittany peninsula is now classified as an area of prevalence for the three species. The methodology used here will allow to anticipate species distribution in the event of future environmental challenges resulting from climate change scenarios.

This map presents all layers corresponding to "Inland freight water transport" activities in the Atlantic area. For more information about this NACE code : https://ec.europa.eu/eurostat/ramon/nomenclatures/index.cfm?TargetUrl=DSP_NOM_DTL_VIEW&StrNom=NACE_REV2&StrLanguageCode=EN&IntPcKey=18512804&IntKey=18513014&StrLayoutCode=HIERARCHIC&IntCurrentPage=1 Indicators collected are : Business indicators per country